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    "# Understanding Proximal Policy Optimization (PPO): A Complete Guide\n",
    "\n",
    "## Table of Contents\n",
    "\n",
    "- [Introduction](#introduction)\n",
    "- [What is PPO?](#what-is-ppo)\n",
    "  - [Why PPO? Advantages over TRPO and Vanilla PG](#why-ppo)\n",
    "- [Where and How PPO is Used](#where-and-how-ppo-is-used)\n",
    "- [Mathematical Foundation of PPO](#mathematical-foundation-of-ppo)\n",
    "  - [Policy Gradient Recap & Surrogate Objective](#policy-gradient-recap--surrogate-objective)\n",
    "  - [The Problem with Large Policy Updates](#the-problem-with-large-policy-updates)\n",
    "  - [The PPO Clipped Surrogate Objective ($L^{CLIP}$)](#the-ppo-clipped-surrogate-objective-lclip)\n",
    "  - [Value Function Loss ($L^{VF}$)](#value-function-loss-lvf)\n",
    "  - [Optional: Entropy Bonus ($S$)](#optional-entropy-bonus-s)\n",
    "  - [The Combined PPO Objective](#the-combined-ppo-objective)\n",
    "  - [Advantage Estimation: GAE](#advantage-estimation-gae)\n",
    "  - [Multiple Update Epochs](#multiple-update-epochs)\n",
    "- [Step-by-Step Explanation of PPO](#step-by-step-explanation-of-ppo)\n",
    "- [Key Components of PPO](#key-components-of-ppo)\n",
    "  - [Policy Network (Actor)](#policy-network-actor)\n",
    "  - [Value Network (Critic)](#value-network-critic)\n",
    "  - [Rollout Collection (On-Policy)](#rollout-collection-on-policy)\n",
    "  - [Advantage Estimation (GAE)](#advantage-estimation-gae)\n",
    "  - [Clipped Surrogate Objective](#clipped-surrogate-objective)\n",
    "  - [Value Function Update](#value-function-update)\n",
    "  - [Multiple Epochs & Mini-batches](#multiple-epochs--mini-batches)\n",
    "  - [Hyperparameters](#hyperparameters)\n",
    "- [PPO and Large Language Models (LLMs) - RLHF](#ppo-and-large-language-models-llms---rlhf)\n",
    "  - [The Alignment Problem](#the-alignment-problem)\n",
    "  - [Reinforcement Learning from Human Feedback (RLHF)](#reinforcement-learning-from-human-feedback-rlhf)\n",
    "  - [How PPO Fits In](#how-ppo-fits-in)\n",
    "  - [The RLHF Objective with PPO](#the-rlhf-objective-with-ppo)\n",
    "  - [Why PPO for LLMs?](#why-ppo-for-llms)\n",
    "  - [Challenges](#challenges)\n",
    "- [Practical Example: Custom Grid World](#practical-example-custom-grid-world)\n",
    "- [Setting up the Environment](#setting-up-the-environment)\n",
    "- [Creating the Custom Environment](#creating-the-custom-environment)\n",
    "- [Implementing the PPO Algorithm](#implementing-the-ppo-algorithm)\n",
    "  - [Defining the Actor Network](#defining-the-actor-network)\n",
    "  - [Defining the Critic Network](#defining-the-critic-network)\n",
    "  - [Calculating Generalized Advantage Estimation (GAE)](#calculating-generalized-advantage-estimation-gae)\n",
    "  - [The PPO Update Step](#the-ppo-update-step)\n",
    "- [Running the PPO Algorithm](#running-the-ppo-algorithm)\n",
    "  - [Hyperparameter Setup](#hyperparameter-setup)\n",
    "  - [Initialization](#initialization)\n",
    "  - [Training Loop](#training-loop)\n",
    "- [Visualizing the Learning Process](#visualizing-the-learning-process)\n",
    "- [Analyzing the Learned Policy (Optional Visualization)](#analyzing-the-learned-policy-optional-visualization)\n",
    "- [Common Challenges and Solutions in PPO](#common-challenges-and-solutions-in-ppo)\n",
    "- [Conclusion](#conclusion)\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Proximal Policy Optimization (PPO) is a state-of-the-art policy gradient algorithm that has become a default choice for many reinforcement learning tasks, particularly in continuous control. It builds upon the ideas of Trust Region Policy Optimization (TRPO) but uses a simpler mechanism—primarily a *clipped surrogate objective*—to constrain policy updates and ensure stable learning, making it easier to implement and tune while often achieving comparable or better performance.\n",
    "\n",
    "## What is PPO?\n",
    "\n",
    "PPO is an **on-policy**, **actor-critic** algorithm. Like other policy gradient methods, it directly learns a policy $\\pi(a|s; \\theta)$. Key characteristics include:\n",
    "\n",
    "1.  **Actor-Critic Structure:** It uses two networks: an Actor (policy network $\\pi_\theta$) to select actions and a Critic (value network $V_\\phi$) to evaluate states and help estimate advantages.\n",
    "2.  **On-Policy Data Collection:** It collects trajectories of experience using the *current* policy.\n",
    "3.  **Clipped Surrogate Objective:** Instead of TRPO's complex KL constraint and second-order optimization, PPO uses a simpler first-order objective function that penalizes large changes in the policy probability ratio $r_t(\\theta) = \\frac{\\pi_\\theta(a_t|s_t)}{\\pi_{\\theta_{old}}(a_t|s_t)}$. This clipping mechanism effectively keeps the new policy close to the old policy.\n",
    "4.  **Multiple Update Epochs:** PPO typically performs multiple epochs of gradient updates on the same batch of collected data, significantly improving sample efficiency compared to methods that discard data after one update (like REINFORCE or standard A2C).\n",
    "\n",
    "### Why PPO? Advantages over TRPO and Vanilla PG\n",
    "\n",
    "- **Simplicity:** PPO's clipped objective is much easier to implement than TRPO's FVP, CG, and line search. It uses standard stochastic gradient ascent.\n",
    "- **Stability:** The clipping mechanism provides similar stability benefits to TRPO's trust region, preventing destructive large policy updates that can plague vanilla policy gradients.\n",
    "- **Efficiency:** Generally more sample efficient than vanilla policy gradients due to multiple updates per batch, and often computationally faster per update than TRPO.\n",
    "- **Performance:** Achieves state-of-the-art results across a wide range of benchmarks, especially in continuous control.\n",
    "\n",
    "## Where and How PPO is Used\n",
    "\n",
    "PPO is one of the most popular and widely used RL algorithms today:\n",
    "\n",
    "1.  **Continuous Control:** Robotics simulation (MuJoCo), locomotion, manipulation tasks.\n",
    "2.  **Video Games:** Complex games requiring sophisticated policies (e.g., Dota 2, StarCraft).\n",
    "3.  **Large Language Model Alignment:** Used in Reinforcement Learning from Human Feedback (RLHF) to fine-tune LLMs like ChatGPT.\n",
    "4.  **General RL Benchmarks:** Often serves as a strong baseline for new algorithm development.\n",
    "\n",
    "PPO is suitable when:\n",
    "- Stable and robust learning is desired.\n",
    "- Implementation simplicity is valued over TRPO's theoretical guarantees.\n",
    "- The action space is discrete or continuous.\n",
    "- On-policy interaction is feasible.\n",
    "- Good sample efficiency (relative to other on-policy methods) is needed.\n",
    "\n",
    "## Mathematical Foundation of PPO\n",
    "\n",
    "### Policy Gradient Recap & Surrogate Objective\n",
    "Recall TRPO's surrogate objective using importance sampling:\n",
    "$$ L_{\theta_{old}}(\\theta) = \\mathbb{E}_{t} \\left[ \\frac{\\pi_\\theta(a_t|s_t)}{\\pi_{\\theta_{old}}(a_t|s_t)} \\hat{A}_t \\right] = \\mathbb{E}_{t} [ r_t(\\theta) \\hat{A}_t ] $$\n",
    "where $r_t(\\theta) = \\frac{\\pi_\\theta(a_t|s_t)}{\\pi_{\\theta_{old}}(a_t|s_t)}$ is the probability ratio and $\\hat{A}_t$ is the estimated advantage under the old policy $\\pi_{\\theta_{old}}$.\n",
    "\n",
    "### The Problem with Large Policy Updates\n",
    "Maximizing $L_{\theta_{old}}(\\theta)$ directly with large steps can be problematic because if $r_t(\\theta)$ becomes very large or small, the update becomes unstable. TRPO solved this with a KL constraint.\n",
    "\n",
    "### The PPO Clipped Surrogate Objective ($L^{CLIP}$)\n",
    "PPO introduces a simpler way to discourage large ratios $r_t(\\theta)$ by clipping the objective:\n",
    "$$ L^{CLIP}(\\theta) = \\mathbb{E}_t \\left[ \\min \\left( r_t(\\theta) \\hat{A}_t, \\quad \\text{clip}(r_t(\\theta), 1 - \\epsilon, 1 + \\epsilon) \\hat{A}_t \\right) \\right] $$\n",
    "Here:\n",
    "- $\\epsilon$ is a small hyperparameter (e.g., 0.1 or 0.2) defining the clipping range.\n",
    "- $\\text{clip}(r_t(\\theta), 1 - \\epsilon, 1 + \\epsilon)$ restricts the ratio $r_t(\\theta)$ to be within the interval $[1 - \\epsilon, 1 + \\epsilon]$.\n",
    "- The $\\min$ function takes the minimum of the original objective ($r_t(\\theta) \\hat{A}_t$) and the clipped version ($\text{clip}(...) \\hat{A}_t$).\n",
    "\n",
    "**Intuition:**\n",
    "- If $\\hat{A}_t > 0$ (action was better than average): The objective increases as $r_t(\\theta)$ increases (making the action more likely). However, the increase is capped when $r_t(\\theta)$ exceeds $1 + \\epsilon$, preventing overly large updates driven by this single good action.\n",
    "- If $\\hat{A}_t < 0$ (action was worse than average): The objective decreases as $r_t(\\theta)$ increases (making the action less likely). The clipping term $\text{clip}(r_t(\\theta), 1 - \\epsilon, 1 + \\epsilon)$ becomes active when $r_t(\\theta)$ falls below $1 - \\epsilon$. Taking the $\\min$ ensures we use the term that gives a *smaller* (less negative or more positive) objective, effectively limiting how much we reduce the probability of this action in one step.\n",
    "\n",
    "Essentially, the clipping removes the incentive for the policy to change too drastically ($r_t$ moving far from 1.0) in a single update based on the current advantage estimate.\n",
    "\n",
    "### Value Function Loss ($L^{VF}$)\n",
    "Like many actor-critic methods, PPO trains a value network $V_\\phi(s)$ to estimate state values, primarily used for calculating advantages. The value network is trained by minimizing the squared error between its predictions and some target value (often the empirical return or GAE advantage + old value):\n",
    "$$ L^{VF}(\\phi) = \\mathbb{E}_t [(V_\\phi(s_t) - V_t^{targ})^2] $$\n",
    "where $V_t^{targ}$ could be the discounted return $G_t$ or $\\hat{A}_t^{GAE} + V_{\\phi_{old}}(s_t)$.\n",
    "\n",
    "### Optional: Entropy Bonus ($S$)\n",
    "To encourage exploration and prevent premature convergence to suboptimal deterministic policies, an entropy bonus is often added to the objective. The goal is to maximize policy entropy $H(\\pi_\theta(\\cdot|s_t))$.\n",
    "$$ S[\\pi_\theta](s_t) = \\mathbb{E}_{a \\sim \\pi_\theta(\\cdot|s_t)} [-\\log \\pi_\theta(a|s_t)] $$\n",
    "\n",
    "### The Combined PPO Objective\n",
    "The final objective function often combines the policy surrogate loss, the value function loss, and the entropy bonus:\n",
    "$$ L^{PPO}(\\theta, \\phi) = \\mathbb{E}_t [ L^{CLIP}(\\theta) - c_1 L^{VF}(\\phi) + c_2 S[\\pi_\theta](s_t) ] $$\n",
    "where $c_1$ and $c_2$ are coefficients (hyperparameters). Often, the policy and value losses are optimized separately using their respective gradients, although they can share lower network layers.\n",
    "\n",
    "### Advantage Estimation: GAE\n",
    "PPO typically uses Generalized Advantage Estimation (GAE), identical to its use in TRPO, to get stable and low-variance advantage estimates $\\hat{A}_t$.\n",
    "$$ \\hat{A}^{GAE}_t = \\sum_{l=0}^{\\infty} (\\gamma \\lambda)^l \\delta_{t+l}, \\quad \\text{where} \\quad \\delta_t = r_t + \\gamma V(s_{t+1}) - V(s_t) $$\n",
    "\n",
    "### Multiple Update Epochs\n",
    "A key feature of PPO is performing multiple gradient descent steps (epochs) on the same batch of collected experience. This improves sample efficiency. The clipping mechanism prevents the policy from moving too far from $\\pi_{\\theta_{old}}$ even across multiple updates using the same data.\n",
    "\n",
    "## Step-by-Step Explanation of PPO\n",
    "\n",
    "1.  **Initialize**: Policy network $\\pi(a|s; \\theta)$ (Actor), Value network $V(s; \\phi)$ (Critic), hyperparameters ($\\gamma, \\lambda, \\epsilon, c_1, c_2$, learning rates, epochs, batch size/steps per iteration).\n",
    "2.  **For each iteration**:\n",
    "    a.  **Collect Trajectories**: Using the current policy $\\pi_{\\theta_{old}} = \\pi(\\cdot | \\cdot; \\theta)$, collect a batch of trajectories (states, actions, rewards, dones, next_states, and the log probabilities $\\log \\pi_{\\theta_{old}}(a_t|s_t)$).\n",
    "    b.  **Estimate Values & Advantages**: Compute $V(s_t; \\phi)$ for all states. Compute GAE advantages $\\hat{A}_t$ and returns-to-go $V_t^{targ} = \\hat{A}_t + V(s_t; \\phi)$ using the collected data and current value network.\n",
    "    c.  **Optimize (Multiple Epochs)**: For K epochs:\n",
    "        i. Iterate over the collected batch (possibly in mini-batches).\n",
    "        ii. For each mini-batch:\n",
    "            - Calculate the policy ratio $r_t(\\theta) = \\frac{\\pi_\\theta(a_t|s_t)}{\\pi_{\\theta_{old}}(a_t|s_t)} = \\exp(\\log \\pi_\\theta(a_t|s_t) - \\log \\pi_{\\theta_{old}}(a_t|s_t))$.\n",
    "            - Calculate the clipped surrogate objective $L^{CLIP}$.\n",
    "            - Calculate the value function loss $L^{VF}$.\n",
    "            - (Optional) Calculate the entropy bonus $S$.\n",
    "            - Compute the combined loss (e.g., $L = -L^{CLIP} + c_1 L^{VF} - c_2 S$).\n",
    "            - Perform gradient descent step on $\\theta$ and $\\phi$ using the combined loss or separate losses.\n",
    "3.  **Repeat**: Until convergence.\n",
    "\n",
    "## Key Components of PPO\n",
    "\n",
    "### Policy Network (Actor)\n",
    "- Parameterizes the stochastic policy $\\pi(a|s; \\theta)$, outputs action probabilities or parameters of a distribution.\n",
    "\n",
    "### Value Network (Critic)\n",
    "- Parameterizes the state-value function $V(s; \\phi)$, used for GAE.\n",
    "- Trained via MSE loss.\n",
    "\n",
    "### Rollout Collection (On-Policy)\n",
    "- Collects batches of experience using the *current* policy $\\pi_{\\theta_{old}}$.\n",
    "- Stores states, actions, rewards, dones, and $\\log \\pi_{\\theta_{old}}(a|s)$.\n",
    "\n",
    "### Advantage Estimation (GAE)\n",
    "- Computes variance-reduced advantage estimates $\\hat{A}_t$.\n",
    "\n",
    "### Clipped Surrogate Objective\n",
    "- The core of PPO-Clip. Limits the effect of the probability ratio $r_t(\\theta)$ to prevent excessively large updates.\n",
    "\n",
    "### Value Function Update\n",
    "- Updates the critic network based on observed returns/advantages.\n",
    "\n",
    "### Multiple Epochs & Mini-batches\n",
    "- Improves sample efficiency by reusing collected data for multiple gradient updates.\n",
    "- Mini-batching can further stabilize training within epochs.\n",
    "\n",
    "### Hyperparameters\n",
    "- **Clip Range ($\\epsilon$):** Controls how far the new policy can deviate from the old (e.g., 0.1, 0.2).\n",
    "- **GAE Lambda ($\\lambda$):** Controls bias-variance trade-off in advantage estimation (e.g., 0.95, 0.97).\n",
    "- **Discount Factor ($\\gamma$):** Standard RL discount (e.g., 0.99).\n",
    "- **Learning Rate(s):** For actor and critic optimizers (often Adam).\n",
    "- **Number of Epochs (K):** How many times to iterate over the data batch (e.g., 4, 10).\n",
    "- **Mini-batch Size:** Size of mini-batches used within epochs.\n",
    "- **Value Loss Coefficient ($c_1$):** Weight for the value loss term (e.g., 0.5, 1.0).\n",
    "- **Entropy Coefficient ($c_2$):** Weight for the entropy bonus (e.g., 0.01, 0.001).\n",
    "\n",
    "## PPO and Large Language Models (LLMs) - RLHF\n",
    "\n",
    "PPO has become a cornerstone algorithm in aligning Large Language Models (LLMs) with human preferences through a technique called Reinforcement Learning from Human Feedback (RLHF). This addresses the challenge that simply predicting the next token (standard supervised learning) doesn't guarantee that the LLM's output is helpful, harmless, and honest.\n",
    "\n",
    "### The Alignment Problem\n",
    "LLMs trained on vast internet text can generate fluent and knowledgeable responses, but they might also produce undesirable outputs (toxic, biased, factually incorrect, unhelpful). We want to steer the LLM towards generating responses that humans find preferable.\n",
    "\n",
    "### Reinforcement Learning from Human Feedback (RLHF)\n",
    "RLHF is typically a three-stage process:\n",
    "\n",
    "1.  **Supervised Fine-Tuning (SFT):** Start with a pretrained base LLM. Fine-tune it on a smaller, high-quality dataset of prompt-response pairs curated by humans. This adapts the model to the desired style and domain (e.g., instruction following, chat).\n",
    "\n",
    "2.  **Reward Model (RM) Training:**\n",
    "    *   Take prompts and generate multiple responses using the SFT model.\n",
    "    *   Human labelers rank these responses from best to worst based on desired criteria (helpfulness, harmlessness, etc.).\n",
    "    *   Train a separate model (often another LLM, initialized from the SFT model or a smaller variant) to predict these human preference scores. Input is a prompt-response pair, output is a scalar reward signal.\n",
    "\n",
    "3.  **RL Fine-Tuning (PPO):**\n",
    "    *   Use the SFT model as the initial policy.\n",
    "    *   Use PPO to further fine-tune this policy.\n",
    "    *   **Actor:** The LLM being tuned.\n",
    "    *   **State:** The input prompt (context).\n",
    "    *   **Action Space:** The vocabulary of tokens the LLM can generate.\n",
    "    *   **Action:** Generating the next token in the sequence.\n",
    "    *   **Policy $\\pi_\\theta$:** The LLM itself, defining the probability distribution over the next token given the context.\n",
    "    *   **Environment:** The process of generating a complete response token by token, starting from a prompt.\n",
    "    *   **Reward:** At the end of generating a response, the *Reward Model* provides a scalar reward indicating the predicted quality/preference for that *entire* response.\n",
    "    *   **PPO Update:** PPO uses the experience (prompt, generated response, RM reward) to update the LLM's (actor's) parameters ($\theta$) to maximize the expected reward from the RM.\n",
    "\n",
    "### How PPO Fits In\n",
    "In the RLHF stage:\n",
    "- PPO optimizes the LLM policy ($\\pi_\theta$).\n",
    "- Trajectories consist of generating sequences of tokens (actions) given a prompt (state).\n",
    "- The Advantage function $\\hat{A}_t$ relies on the rewards from the RM and potentially a value function (critic) estimating the expected RM reward from intermediate states (sequences of tokens).\n",
    "- The policy update aims to increase the probability of token sequences that lead to high rewards from the RM.\n",
    "\n",
    "### The RLHF Objective with PPO\n",
    "A crucial addition in RLHF is often a KL-divergence penalty added to the PPO objective. This prevents the fine-tuned LLM ($\\pi_\theta$) from deviating too much from the original SFT model ($\\pi_{ref}$), ensuring it retains its language capabilities and doesn't overfit to the reward model (reward hacking).\n",
    "$$ \\text{Objective}_{RLHF} = \\mathbb{E}_{(s,a) \\sim \\pi_\\theta} [ R_{RM}(s, a) - \\beta D_{KL}(\\pi_\\theta(\\cdot|s) || \\pi_{ref}(\\cdot|s)) ] $$\n",
    "Here, $R_{RM}$ is the reward from the reward model, and $\\beta$ controls the strength of the KL penalty. PPO is used to optimize this combined objective (maximizing RM reward while staying close to the reference policy). The $L^{CLIP}$ objective implicitly incorporates the reward term via the advantage estimate, and the KL penalty is added explicitly.\n",
    "\n",
    "### Why PPO for LLMs?\n",
    "- **Stability:** Fine-tuning large models is computationally expensive; stable updates preventing performance collapse are vital.\n",
    "- **Sample Efficiency (Relative):** While still on-policy, the multiple epochs per data batch make better use of the expensive rollouts (generating text and getting RM scores) compared to single-update methods.\n",
    "- **Simplicity & Scalability:** Easier to implement and scale across distributed systems compared to TRPO.\n",
    "\n",
    "### Challenges\n",
    "- **Reward Model Quality:** The entire process hinges on having a reward model that accurately reflects true human preferences.\n",
    "- **Reward Hacking:** The LLM might find ways to exploit the reward model to get high scores without actually improving quality.\n",
    "- **Computational Cost:** RLHF is extremely computationally intensive.\n",
    "- **Hyperparameter Tuning:** Requires careful tuning of both PPO and RLHF-specific parameters (like $\\beta$).\n",
    "\n",
    "In summary, PPO provides the stable and relatively efficient RL optimization engine needed to fine-tune massive LLMs based on learned human preference models in the RLHF pipeline.\n",
    "\n",
    "## Practical Example: Custom Grid World\n",
    "\n",
    "We again use the custom Grid World to illustrate the PPO implementation mechanics, maintaining consistency with the previous notebooks.\n",
    "\n",
    "**Environment Description:** (Same as before)\n",
    "- Grid Size: 10x10.\n",
    "- State: `[row/9, col/9]`\n",
    "- Actions: 4 discrete (Up, Down, Left, Right).\n",
    "- Start: (0, 0), Goal: (9, 9).\n",
    "- Rewards: +10 (goal), -1 (wall), -0.1 (step).\n",
    "- Termination: Goal or max steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up the Environment\n",
    "\n",
    "Import libraries (same as TRPO)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cpu\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import math\n",
    "from collections import namedtuple, deque\n",
    "from itertools import count\n",
    "from typing import List, Tuple, Dict, Optional, Callable\n",
    "import copy # Needed for storing old policy network parameters\n",
    "\n",
    "# Import PyTorch\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.distributions import Categorical\n",
    "\n",
    "# Set up device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# Set random seeds for reproducibility\n",
    "seed = 42\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "torch.manual_seed(seed)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the Custom Environment\n",
    "\n",
    "Reusing the `GridEnvironment` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Custom Grid World Environment (Identical to the one in previous notebooks)\n",
    "class GridEnvironment:\n",
    "    \"\"\"\n",
    "    A simple 10x10 Grid World environment.\n",
    "    State: (row, col) represented as normalized vector [row/9, col/9].\n",
    "    Actions: 0 (up), 1 (down), 2 (left), 3 (right).\n",
    "    Rewards: +10 for reaching the goal, -1 for hitting a wall, -0.1 for each step.\n",
    "    \"\"\"\n",
    "    def __init__(self, rows: int = 10, cols: int = 10) -> None:\n",
    "        self.rows: int = rows\n",
    "        self.cols: int = cols\n",
    "        self.start_state: Tuple[int, int] = (0, 0)\n",
    "        self.goal_state: Tuple[int, int] = (rows - 1, cols - 1)\n",
    "        self.state: Tuple[int, int] = self.start_state\n",
    "        self.state_dim: int = 2\n",
    "        self.action_dim: int = 4\n",
    "        self.action_map: Dict[int, Tuple[int, int]] = {0: (-1, 0), 1: (1, 0), 2: (0, -1), 3: (0, 1)}\n",
    "\n",
    "    def reset(self) -> torch.Tensor:\n",
    "        self.state = self.start_state\n",
    "        return self._get_state_tensor(self.state)\n",
    "\n",
    "    def _get_state_tensor(self, state_tuple: Tuple[int, int]) -> torch.Tensor:\n",
    "        norm_row = state_tuple[0] / (self.rows - 1) if self.rows > 1 else 0.0\n",
    "        norm_col = state_tuple[1] / (self.cols - 1) if self.cols > 1 else 0.0\n",
    "        normalized_state: List[float] = [norm_row, norm_col]\n",
    "        return torch.tensor(normalized_state, dtype=torch.float32, device=device)\n",
    "\n",
    "    def step(self, action: int) -> Tuple[torch.Tensor, float, bool]:\n",
    "        if self.state == self.goal_state:\n",
    "            return self._get_state_tensor(self.state), 0.0, True\n",
    "        dr, dc = self.action_map[action]\n",
    "        current_row, current_col = self.state\n",
    "        next_row, next_col = current_row + dr, current_col + dc\n",
    "        reward: float = -0.1\n",
    "        hit_wall: bool = False\n",
    "        if not (0 <= next_row < self.rows and 0 <= next_col < self.cols):\n",
    "            next_row, next_col = current_row, current_col\n",
    "            reward = -1.0\n",
    "            hit_wall = True\n",
    "        self.state = (next_row, next_col)\n",
    "        next_state_tensor: torch.Tensor = self._get_state_tensor(self.state)\n",
    "        done: bool = (self.state == self.goal_state)\n",
    "        if done:\n",
    "            reward = 10.0\n",
    "        return next_state_tensor, reward, done\n",
    "\n",
    "    def get_action_space_size(self) -> int:\n",
    "        return self.action_dim\n",
    "\n",
    "    def get_state_dimension(self) -> int:\n",
    "        return self.state_dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate and test the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Custom Grid Environment:\n",
      "Size: 10x10\n",
      "State Dim: 2\n",
      "Action Dim: 4\n",
      "Example state tensor for (0,0): tensor([0., 0.])\n"
     ]
    }
   ],
   "source": [
    "custom_env = GridEnvironment(rows=10, cols=10)\n",
    "n_actions_custom = custom_env.get_action_space_size()\n",
    "n_observations_custom = custom_env.get_state_dimension()\n",
    "\n",
    "print(f\"Custom Grid Environment:\")\n",
    "print(f\"Size: {custom_env.rows}x{custom_env.cols}\")\n",
    "print(f\"State Dim: {n_observations_custom}\")\n",
    "print(f\"Action Dim: {n_actions_custom}\")\n",
    "start_state_tensor = custom_env.reset()\n",
    "print(f\"Example state tensor for (0,0): {start_state_tensor}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing the PPO Algorithm\n",
    "\n",
    "Define the Actor (Policy Network) and Critic (Value Network), GAE calculation, and the PPO update function.\n",
    "\n",
    "### Defining the Actor Network\n",
    "\n",
    "Identical to the Policy Network used in TRPO/REINFORCE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Policy Network (Actor)\n",
    "class PolicyNetwork(nn.Module):\n",
    "    \"\"\" MLP Actor network for PPO \"\"\"\n",
    "    def __init__(self, n_observations: int, n_actions: int):\n",
    "        super(PolicyNetwork, self).__init__()\n",
    "        self.layer1 = nn.Linear(n_observations, 128)\n",
    "        self.layer2 = nn.Linear(128, 128)\n",
    "        self.layer3 = nn.Linear(128, n_actions)\n",
    "\n",
    "    def forward(self, x: torch.Tensor) -> Categorical:\n",
    "        \"\"\"\n",
    "        Forward pass, returns a Categorical distribution.\n",
    "        \"\"\"\n",
    "        if not isinstance(x, torch.Tensor):\n",
    "             x = torch.tensor(x, dtype=torch.float32, device=device)\n",
    "        elif x.dtype != torch.float32:\n",
    "             x = x.to(dtype=torch.float32)\n",
    "        if x.dim() == 1:\n",
    "            x = x.unsqueeze(0)\n",
    "            \n",
    "        x = F.relu(self.layer1(x))\n",
    "        x = F.relu(self.layer2(x))\n",
    "        action_logits = self.layer3(x)\n",
    "        return Categorical(logits=action_logits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Critic Network\n",
    "\n",
    "Identical to the Value Network used in TRPO."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Value Network (Critic)\n",
    "class ValueNetwork(nn.Module):\n",
    "    \"\"\" MLP Critic network for PPO \"\"\"\n",
    "    def __init__(self, n_observations: int):\n",
    "        super(ValueNetwork, self).__init__()\n",
    "        self.layer1 = nn.Linear(n_observations, 128)\n",
    "        self.layer2 = nn.Linear(128, 128)\n",
    "        self.layer3 = nn.Linear(128, 1)\n",
    "\n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        \"\"\"\n",
    "        Forward pass, returns the estimated state value.\n",
    "        \"\"\"\n",
    "        if not isinstance(x, torch.Tensor):\n",
    "             x = torch.tensor(x, dtype=torch.float32, device=device)\n",
    "        elif x.dtype != torch.float32:\n",
    "             x = x.to(dtype=torch.float32)\n",
    "        if x.dim() == 1:\n",
    "            x = x.unsqueeze(0)\n",
    "            \n",
    "        x = F.relu(self.layer1(x))\n",
    "        x = F.relu(self.layer2(x))\n",
    "        state_value = self.layer3(x)\n",
    "        return state_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating Generalized Advantage Estimation (GAE)\n",
    "\n",
    "Reusing the GAE function from the TRPO implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the compute_gae function that's currently missing\n",
    "def compute_gae(rewards: torch.Tensor, \n",
    "               values: torch.Tensor, \n",
    "               next_values: torch.Tensor, \n",
    "               dones: torch.Tensor, \n",
    "               gamma: float, \n",
    "               lambda_gae: float, \n",
    "               standardize: bool = True) -> torch.Tensor:\n",
    "    \"\"\"\n",
    "    Computes Generalized Advantage Estimation (GAE).\n",
    "    \"\"\"\n",
    "    advantages = torch.zeros_like(rewards)\n",
    "    last_advantage = 0.0\n",
    "\n",
    "    for t in reversed(range(len(rewards))):\n",
    "        mask = 1.0 - dones[t]\n",
    "        delta = rewards[t] + gamma * next_values[t] * mask - values[t]\n",
    "        advantages[t] = delta + gamma * lambda_gae * last_advantage * mask\n",
    "        last_advantage = advantages[t]\n",
    "\n",
    "    if standardize:\n",
    "        mean_adv = torch.mean(advantages)\n",
    "        std_adv = torch.std(advantages) + 1e-8\n",
    "        advantages = (advantages - mean_adv) / std_adv\n",
    "        \n",
    "    return advantages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The PPO Update Step\n",
    "\n",
    "This function performs the core PPO update, iterating over the collected batch for multiple epochs, calculating the clipped surrogate objective and value loss, and updating both actor and critic networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_ppo(actor: PolicyNetwork,\n",
    "               critic: ValueNetwork,\n",
    "               actor_optimizer: optim.Optimizer,\n",
    "               critic_optimizer: optim.Optimizer,\n",
    "               states: torch.Tensor,\n",
    "               actions: torch.Tensor,\n",
    "               log_probs_old: torch.Tensor,\n",
    "               advantages: torch.Tensor,\n",
    "               returns_to_go: torch.Tensor,\n",
    "               ppo_epochs: int,\n",
    "               ppo_clip_epsilon: float,\n",
    "               value_loss_coeff: float,\n",
    "               entropy_coeff: float) -> Tuple[float, float, float]: # Return avg losses\n",
    "    \"\"\"\n",
    "    Performs the PPO update for multiple epochs over the collected batch.\n",
    "\n",
    "    Parameters:\n",
    "    - actor, critic: The networks.\n",
    "    - actor_optimizer, critic_optimizer: The optimizers.\n",
    "    - states, actions, log_probs_old, advantages, returns_to_go: Batch data tensors.\n",
    "    - ppo_epochs (int): Number of optimization epochs.\n",
    "    - ppo_clip_epsilon (float): Clipping parameter epsilon.\n",
    "    - value_loss_coeff (float): Coefficient for the value loss.\n",
    "    - entropy_coeff (float): Coefficient for the entropy bonus.\n",
    "\n",
    "    Returns:\n",
    "    - Tuple[float, float, float]: Average policy loss, value loss, and entropy over the epochs.\n",
    "    \"\"\"\n",
    "    total_policy_loss = 0.0\n",
    "    total_value_loss = 0.0\n",
    "    total_entropy = 0.0\n",
    "\n",
    "    # Detach advantages and old log probs - they are treated as constants during the update\n",
    "    advantages = advantages.detach()\n",
    "    log_probs_old = log_probs_old.detach()\n",
    "    returns_to_go = returns_to_go.detach()\n",
    "\n",
    "    for _ in range(ppo_epochs):\n",
    "        # --- Actor (Policy) Update --- \n",
    "        # Evaluate current policy\n",
    "        policy_dist = actor(states)\n",
    "        log_probs_new = policy_dist.log_prob(actions)\n",
    "        entropy = policy_dist.entropy().mean() # Entropy for exploration bonus\n",
    "        \n",
    "        # Calculate ratio r_t(theta)\n",
    "        ratio = torch.exp(log_probs_new - log_probs_old)\n",
    "        \n",
    "        # Calculate surrogate objectives\n",
    "        surr1 = ratio * advantages\n",
    "        surr2 = torch.clamp(ratio, 1.0 - ppo_clip_epsilon, 1.0 + ppo_clip_epsilon) * advantages\n",
    "        \n",
    "        # PPO Clipped Policy Loss (negative because optimizer minimizes)\n",
    "        # We add the entropy bonus (maximize entropy -> minimize negative entropy)\n",
    "        policy_loss = -torch.min(surr1, surr2).mean() - entropy_coeff * entropy\n",
    "        \n",
    "        # Optimize the actor\n",
    "        actor_optimizer.zero_grad()\n",
    "        policy_loss.backward()\n",
    "        actor_optimizer.step()\n",
    "        \n",
    "        # --- Critic (Value) Update --- \n",
    "        # Predict values\n",
    "        values_pred = critic(states).squeeze()\n",
    "        \n",
    "        # Value loss (MSE)\n",
    "        value_loss = F.mse_loss(values_pred, returns_to_go)\n",
    "        \n",
    "        # Optimize the critic\n",
    "        critic_optimizer.zero_grad()\n",
    "        # Scale value loss before backward pass\n",
    "        (value_loss_coeff * value_loss).backward()\n",
    "        critic_optimizer.step()\n",
    "        \n",
    "        # Accumulate losses for logging\n",
    "        total_policy_loss += policy_loss.item() # Log negative clipped objective + entropy bonus\n",
    "        total_value_loss += value_loss.item()\n",
    "        total_entropy += entropy.item()\n",
    "        \n",
    "    # Calculate average losses over the epochs\n",
    "    avg_policy_loss = total_policy_loss / ppo_epochs\n",
    "    avg_value_loss = total_value_loss / ppo_epochs\n",
    "    avg_entropy = total_entropy / ppo_epochs\n",
    "    \n",
    "    return avg_policy_loss, avg_value_loss, avg_entropy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running the PPO Algorithm\n",
    "\n",
    "Set up hyperparameters, initialize networks and optimizers, and run the PPO training loop.\n",
    "\n",
    "### Hyperparameter Setup\n",
    "\n",
    "Define PPO hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters for PPO on Custom Grid World\n",
    "GAMMA_PPO = 0.99             # Discount factor\n",
    "GAE_LAMBDA_PPO = 0.95        # GAE lambda parameter\n",
    "PPO_CLIP_EPSILON = 0.2       # PPO clipping epsilon\n",
    "ACTOR_LR = 3e-4              # Learning rate for the actor\n",
    "CRITIC_LR_PPO = 1e-3         # Learning rate for the critic\n",
    "PPO_EPOCHS = 10              # Number of optimization epochs per iteration\n",
    "VALUE_LOSS_COEFF = 0.5       # Coefficient for value loss\n",
    "ENTROPY_COEFF = 0.01         # Coefficient for entropy bonus\n",
    "STANDARDIZE_ADV_PPO = True   # Whether to standardize advantages\n",
    "\n",
    "NUM_ITERATIONS_PPO = 150     # Number of PPO iterations (policy updates)\n",
    "STEPS_PER_ITERATION_PPO = 1000 # Steps collected per iteration\n",
    "MAX_STEPS_PER_EPISODE_PPO = 200 # Max steps per episode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialization\n",
    "\n",
    "Initialize actor, critic, and their optimizers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-instantiate the environment\n",
    "custom_env: GridEnvironment = GridEnvironment(rows=10, cols=10)\n",
    "n_actions_custom: int = custom_env.get_action_space_size()\n",
    "n_observations_custom: int = custom_env.get_state_dimension()\n",
    "\n",
    "# Initialize Actor and Critic\n",
    "actor_ppo: PolicyNetwork = PolicyNetwork(n_observations_custom, n_actions_custom).to(device)\n",
    "critic_ppo: ValueNetwork = ValueNetwork(n_observations_custom).to(device)\n",
    "\n",
    "# Initialize Optimizers\n",
    "actor_optimizer_ppo: optim.Adam = optim.Adam(actor_ppo.parameters(), lr=ACTOR_LR)\n",
    "critic_optimizer_ppo: optim.Adam = optim.Adam(critic_ppo.parameters(), lr=CRITIC_LR_PPO)\n",
    "\n",
    "# Lists for plotting\n",
    "ppo_iteration_rewards = []\n",
    "ppo_iteration_avg_ep_lens = []\n",
    "ppo_iteration_policy_losses = []\n",
    "ppo_iteration_value_losses = []\n",
    "ppo_iteration_entropies = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loop\n",
    "\n",
    "The PPO training loop: collect data, compute advantages/returns, update actor and critic over multiple epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting PPO Training on Custom Grid World...\n",
      "Iter 10/150 | Avg Reward: 6.36 | Avg Len: 21.3 | P_Loss: -0.0145 | V_Loss: 0.9804 | Entropy: 0.8628\n",
      "Iter 20/150 | Avg Reward: 7.09 | Avg Len: 18.9 | P_Loss: -0.0118 | V_Loss: 1.0018 | Entropy: 0.6925\n",
      "Iter 30/150 | Avg Reward: 7.61 | Avg Len: 18.5 | P_Loss: -0.0146 | V_Loss: 0.9844 | Entropy: 0.6553\n",
      "Iter 40/150 | Avg Reward: 7.82 | Avg Len: 18.2 | P_Loss: -0.0076 | V_Loss: 0.9962 | Entropy: 0.6224\n",
      "Iter 50/150 | Avg Reward: 7.89 | Avg Len: 18.2 | P_Loss: -0.0079 | V_Loss: 0.9865 | Entropy: 0.6108\n",
      "Iter 60/150 | Avg Reward: 7.92 | Avg Len: 18.2 | P_Loss: -0.0082 | V_Loss: 0.9977 | Entropy: 0.5717\n",
      "Iter 70/150 | Avg Reward: 7.82 | Avg Len: 18.2 | P_Loss: -0.0062 | V_Loss: 1.0129 | Entropy: 0.5540\n",
      "Iter 80/150 | Avg Reward: 7.92 | Avg Len: 18.2 | P_Loss: -0.0085 | V_Loss: 1.0051 | Entropy: 0.5434\n",
      "Iter 90/150 | Avg Reward: 8.01 | Avg Len: 17.9 | P_Loss: -0.0064 | V_Loss: 0.9879 | Entropy: 0.5104\n",
      "Iter 100/150 | Avg Reward: 8.07 | Avg Len: 17.9 | P_Loss: -0.0052 | V_Loss: 0.9288 | Entropy: 0.4867\n",
      "Iter 110/150 | Avg Reward: 8.04 | Avg Len: 17.9 | P_Loss: -0.0066 | V_Loss: 1.0013 | Entropy: 0.4765\n",
      "Iter 120/150 | Avg Reward: 8.12 | Avg Len: 18.2 | P_Loss: -0.0069 | V_Loss: 0.9964 | Entropy: 0.4301\n",
      "Iter 130/150 | Avg Reward: 8.12 | Avg Len: 17.9 | P_Loss: -0.0044 | V_Loss: 0.9387 | Entropy: 0.4038\n",
      "Iter 140/150 | Avg Reward: 8.02 | Avg Len: 17.9 | P_Loss: -0.0044 | V_Loss: 1.0230 | Entropy: 0.3629\n",
      "Iter 150/150 | Avg Reward: 8.07 | Avg Len: 17.9 | P_Loss: -0.0067 | V_Loss: 1.0051 | Entropy: 0.2982\n",
      "Custom Grid World Training Finished (PPO).\n"
     ]
    }
   ],
   "source": [
    "print(\"Starting PPO Training on Custom Grid World...\")\n",
    "\n",
    "# --- PPO Training Loop ---\n",
    "for iteration in range(NUM_ITERATIONS_PPO):\n",
    "    # --- 1. Collect Trajectories (Rollout Phase) --- \n",
    "    # Store data in lists temporarily\n",
    "    batch_states_list = []\n",
    "    batch_actions_list = []\n",
    "    batch_log_probs_old_list = []\n",
    "    batch_rewards_list = []\n",
    "    batch_values_list = []\n",
    "    batch_dones_list = []\n",
    "    \n",
    "    episode_rewards_in_iter = []\n",
    "    episode_lengths_in_iter = []\n",
    "    steps_collected = 0\n",
    "\n",
    "    while steps_collected < STEPS_PER_ITERATION_PPO:\n",
    "        state = custom_env.reset()\n",
    "        episode_reward = 0.0\n",
    "        episode_steps = 0\n",
    "        done = False\n",
    "        \n",
    "        for t in range(MAX_STEPS_PER_EPISODE_PPO):\n",
    "            # Sample action and get value estimate\n",
    "            with torch.no_grad():\n",
    "                policy_dist = actor_ppo(state)\n",
    "                value = critic_ppo(state).squeeze()\n",
    "                \n",
    "                action_tensor = policy_dist.sample()\n",
    "                action = action_tensor.item()\n",
    "                log_prob = policy_dist.log_prob(action_tensor)\n",
    "            \n",
    "            # Interact with environment\n",
    "            next_state, reward, done = custom_env.step(action)\n",
    "            \n",
    "            # Store data\n",
    "            batch_states_list.append(state)\n",
    "            batch_actions_list.append(action)\n",
    "            batch_log_probs_old_list.append(log_prob)\n",
    "            batch_values_list.append(value)\n",
    "            batch_rewards_list.append(reward)\n",
    "            batch_dones_list.append(float(done))\n",
    "            \n",
    "            state = next_state\n",
    "            episode_reward += reward\n",
    "            steps_collected += 1\n",
    "            episode_steps += 1\n",
    "            \n",
    "            if done or steps_collected >= STEPS_PER_ITERATION_PPO:\n",
    "                episode_rewards_in_iter.append(episode_reward)\n",
    "                episode_lengths_in_iter.append(episode_steps)\n",
    "                break\n",
    "                \n",
    "            if steps_collected >= STEPS_PER_ITERATION_PPO:\n",
    "                break\n",
    "    \n",
    "    # --- End Rollout --- \n",
    "\n",
    "    # Calculate next_values for GAE\n",
    "    # For non-terminal states, next_value is the value of the next state\n",
    "    # For terminal states, next_value is 0\n",
    "    next_values = []\n",
    "    with torch.no_grad():\n",
    "        for i in range(len(batch_states_list)):\n",
    "            if batch_dones_list[i] > 0.5:  # If done\n",
    "                next_values.append(torch.tensor(0.0))\n",
    "            elif i == len(batch_states_list) - 1:  # Last state in batch\n",
    "                next_state = custom_env.step(batch_actions_list[i])[0]  # Get next state\n",
    "                next_values.append(critic_ppo(next_state).squeeze())\n",
    "            else:  # Not done, use value of next state in batch\n",
    "                next_values.append(batch_values_list[i+1])\n",
    "\n",
    "    # Convert lists to tensors\n",
    "    states_tensor = torch.stack(batch_states_list).to(device)\n",
    "    actions_tensor = torch.tensor(batch_actions_list, dtype=torch.long, device=device)\n",
    "    log_probs_old_tensor = torch.stack(batch_log_probs_old_list).squeeze().to(device)\n",
    "    rewards_tensor = torch.tensor(batch_rewards_list, dtype=torch.float32, device=device)\n",
    "    values_tensor = torch.stack(batch_values_list).to(device)\n",
    "    next_values_tensor = torch.stack(next_values).to(device)\n",
    "    dones_tensor = torch.tensor(batch_dones_list, dtype=torch.float32, device=device)\n",
    "\n",
    "    # --- 2. Estimate Advantages & Returns-to-go --- \n",
    "    advantages_tensor = compute_gae(\n",
    "        rewards_tensor, values_tensor, next_values_tensor, dones_tensor, \n",
    "        GAMMA_PPO, GAE_LAMBDA_PPO, standardize=STANDARDIZE_ADV_PPO\n",
    "    )\n",
    "    returns_to_go_tensor = advantages_tensor + values_tensor\n",
    "\n",
    "    # --- 3. Perform PPO Update --- \n",
    "    avg_policy_loss, avg_value_loss, avg_entropy = update_ppo(\n",
    "        actor_ppo, critic_ppo, actor_optimizer_ppo, critic_optimizer_ppo,\n",
    "        states_tensor, actions_tensor, log_probs_old_tensor,\n",
    "        advantages_tensor, returns_to_go_tensor,\n",
    "        PPO_EPOCHS, PPO_CLIP_EPSILON, VALUE_LOSS_COEFF, ENTROPY_COEFF\n",
    "    )\n",
    "\n",
    "    # --- Logging --- \n",
    "    avg_reward_iter = np.mean(episode_rewards_in_iter) if episode_rewards_in_iter else np.nan\n",
    "    avg_len_iter = np.mean(episode_lengths_in_iter) if episode_lengths_in_iter else np.nan\n",
    "\n",
    "    ppo_iteration_rewards.append(avg_reward_iter)\n",
    "    ppo_iteration_avg_ep_lens.append(avg_len_iter)\n",
    "    ppo_iteration_policy_losses.append(avg_policy_loss)\n",
    "    ppo_iteration_value_losses.append(avg_value_loss)\n",
    "    ppo_iteration_entropies.append(avg_entropy)\n",
    "\n",
    "    if (iteration + 1) % 10 == 0:\n",
    "        print(f\"Iter {iteration+1}/{NUM_ITERATIONS_PPO} | Avg Reward: {avg_reward_iter:.2f} | Avg Len: {avg_len_iter:.1f} | P_Loss: {avg_policy_loss:.4f} | V_Loss: {avg_value_loss:.4f} | Entropy: {avg_entropy:.4f}\")\n",
    "\n",
    "print(\"Custom Grid World Training Finished (PPO).\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Learning Process\n",
    "\n",
    "Plot the results for the PPO agent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting results for PPO on Custom Grid World\n",
    "plt.figure(figsize=(20, 8))\n",
    "\n",
    "# Average Rewards per Iteration\n",
    "plt.subplot(2, 3, 1)\n",
    "valid_rewards_ppo = [r for r in ppo_iteration_rewards if not np.isnan(r)]\n",
    "valid_indices_ppo = [i for i, r in enumerate(ppo_iteration_rewards) if not np.isnan(r)]\n",
    "plt.plot(valid_indices_ppo, valid_rewards_ppo)\n",
    "plt.title('PPO Custom Grid: Avg Ep Reward / Iteration')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Avg Reward')\n",
    "plt.grid(True)\n",
    "if len(valid_rewards_ppo) >= 10:\n",
    "    rewards_ma_ppo = np.convolve(valid_rewards_ppo, np.ones(10)/10, mode='valid')\n",
    "    plt.plot(valid_indices_ppo[9:], rewards_ma_ppo, label='10-iter MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "# Average Episode Length per Iteration\n",
    "plt.subplot(2, 3, 2)\n",
    "valid_lens_ppo = [l for l in ppo_iteration_avg_ep_lens if not np.isnan(l)]\n",
    "valid_indices_len_ppo = [i for i, l in enumerate(ppo_iteration_avg_ep_lens) if not np.isnan(l)]\n",
    "plt.plot(valid_indices_len_ppo, valid_lens_ppo)\n",
    "plt.title('PPO Custom Grid: Avg Ep Length / Iteration')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Avg Steps')\n",
    "plt.grid(True)\n",
    "if len(valid_lens_ppo) >= 10:\n",
    "    lens_ma_ppo = np.convolve(valid_lens_ppo, np.ones(10)/10, mode='valid')\n",
    "    plt.plot(valid_indices_len_ppo[9:], lens_ma_ppo, label='10-iter MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "# Critic (Value) Loss per Iteration\n",
    "plt.subplot(2, 3, 3)\n",
    "plt.plot(ppo_iteration_value_losses)\n",
    "plt.title('PPO Custom Grid: Avg Value Loss / Iteration')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('MSE Loss')\n",
    "plt.grid(True)\n",
    "if len(ppo_iteration_value_losses) >= 10:\n",
    "    vloss_ma_ppo = np.convolve(ppo_iteration_value_losses, np.ones(10)/10, mode='valid')\n",
    "    plt.plot(np.arange(len(vloss_ma_ppo)) + 9, vloss_ma_ppo, label='10-iter MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "# Actor (Policy) Loss per Iteration\n",
    "plt.subplot(2, 3, 4)\n",
    "# Plotting the negative loss (since we minimized -L_clip - entropy)\n",
    "plt.plot([-l for l in ppo_iteration_policy_losses]) \n",
    "plt.title('PPO Custom Grid: Avg Policy Objective / Iteration')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Avg (-Policy Loss)') \n",
    "plt.grid(True)\n",
    "if len(ppo_iteration_policy_losses) >= 10:\n",
    "    ploss_ma_ppo = np.convolve([-l for l in ppo_iteration_policy_losses], np.ones(10)/10, mode='valid')\n",
    "    plt.plot(np.arange(len(ploss_ma_ppo)) + 9, ploss_ma_ppo, label='10-iter MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "# Entropy per Iteration\n",
    "plt.subplot(2, 3, 5)\n",
    "plt.plot(ppo_iteration_entropies)\n",
    "plt.title('PPO Custom Grid: Avg Policy Entropy / Iteration')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Entropy')\n",
    "plt.grid(True)\n",
    "if len(ppo_iteration_entropies) >= 10:\n",
    "    entropy_ma_ppo = np.convolve(ppo_iteration_entropies, np.ones(10)/10, mode='valid')\n",
    "    plt.plot(np.arange(len(entropy_ma_ppo)) + 9, entropy_ma_ppo, label='10-iter MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, So let's do a concise analysis of the PPO learning curves for the Custom Grid World:\n",
    "\n",
    "**Analysis of PPO Learning Curves (Custom Grid World):**\n",
    "\n",
    "1.  **Avg Ep Reward / Iteration:**\n",
    "    The agent demonstrates rapid and stable learning, with average rewards quickly increasing in the first ~20 iterations to reach near-optimal levels (around 8). The curve shows much less variance than REINFORCE and converges smoothly, highlighting PPO's stability gained through the clipped objective and actor-critic structure. It remains consistently high post-convergence.\n",
    "\n",
    "2.  **Avg Ep Length / Iteration:**\n",
    "    Mirroring the rewards, the episode length plummets within the first ~20 iterations, converging swiftly to the optimal path length (around 18 steps). This shows the agent quickly learned an efficient navigation policy. The stability at the optimal length indicates a robust and converged policy.\n",
    "\n",
    "3.  **Avg Value Loss / Iteration:**\n",
    "    The value function (critic) loss fluctuates throughout training but remains relatively stable on average after an initial adjustment period (settling around 0.98-1.00). This suggests the critic is adequately tracking the value of states under the improving policy, providing stable advantage estimates for the actor updates, even if not strictly converging to zero MSE. *Note: The absolute value seems high for typical MSE; it might represent something slightly different or indicate scaling.*\n",
    "\n",
    "4.  **Avg Policy Objective / Iteration:**\n",
    "    The policy loss (representing the PPO clipped surrogate objective) shows a clear downward trend, especially noticeable in the moving average. This indicates the policy network is successfully optimizing the objective function over iterations. While still exhibiting some variance, it's significantly more stable than the REINFORCE loss plot.\n",
    "\n",
    "5.  **Avg Policy Entropy / Iteration:**\n",
    "    Policy entropy starts high and steadily decreases over training, indicating the policy becomes more deterministic and confident in its actions as it learns. This is expected behaviour; the agent reduces exploration as it identifies better actions. The smooth decrease suggests a controlled convergence without premature collapse of exploration.\n",
    "\n",
    "**Overall Conclusion:**\n",
    "PPO demonstrates strong performance on the Grid World, achieving rapid convergence to an optimal and efficient policy, similar in speed to REINFORCE but with significantly improved stability in rewards and loss curves. The value function learns adequately to support policy improvement, and the policy entropy decreases appropriately. These results showcase PPO's effectiveness in balancing exploration and exploitation while maintaining stable updates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing the Learned Policy (Optional Visualization)\n",
    "\n",
    "Visualize the policy learned by the PPO actor network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Plotting Learned Policy from PPO:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reusing the policy plotting function (works for any network outputting Categorical)\n",
    "def plot_ppo_policy_grid(policy_net: PolicyNetwork, env: GridEnvironment, device: torch.device) -> None:\n",
    "    \"\"\"\n",
    "    Plots the greedy policy derived from the PPO policy network.\n",
    "    Shows the most likely action for each state.\n",
    "    (Identical to the REINFORCE/TRPO plotting function)\n",
    "    \"\"\"\n",
    "    rows: int = env.rows\n",
    "    cols: int = env.cols\n",
    "    policy_grid: np.ndarray = np.empty((rows, cols), dtype=str)\n",
    "    action_symbols: Dict[int, str] = {0: '↑', 1: '↓', 2: '←', 3: '→'}\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(cols * 0.6, rows * 0.6))\n",
    "\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            state_tuple: Tuple[int, int] = (r, c)\n",
    "            if state_tuple == env.goal_state:\n",
    "                policy_grid[r, c] = 'G'\n",
    "                ax.text(c, r, 'G', ha='center', va='center', color='green', fontsize=12, weight='bold')\n",
    "            else:\n",
    "                state_tensor: torch.Tensor = env._get_state_tensor(state_tuple)\n",
    "                with torch.no_grad():\n",
    "                    action_dist: Categorical = policy_net(state_tensor)\n",
    "                    best_action: int = action_dist.probs.argmax(dim=1).item()\n",
    "\n",
    "                policy_grid[r, c] = action_symbols[best_action]\n",
    "                ax.text(c, r, policy_grid[r, c], ha='center', va='center', color='black', fontsize=12)\n",
    "\n",
    "    ax.matshow(np.zeros((rows, cols)), cmap='Greys', alpha=0.1)\n",
    "    ax.set_xticks(np.arange(-.5, cols, 1), minor=True)\n",
    "    ax.set_yticks(np.arange(-.5, rows, 1), minor=True)\n",
    "    ax.grid(which='minor', color='black', linestyle='-', linewidth=1)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_title(\"PPO Learned Policy (Most Likely Action)\")\n",
    "    plt.show()\n",
    "\n",
    "# Plot the policy learned by the trained PPO actor\n",
    "print(\"\\nPlotting Learned Policy from PPO:\")\n",
    "plot_ppo_policy_grid(actor_ppo, custom_env, device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Common Challenges and Solutions in PPO\n",
    "\n",
    "**Challenge: Sensitivity to Hyperparameters**\n",
    "*   **Problem:** Performance can still be sensitive to the choice of clipping range ($\\epsilon$), learning rates, number of epochs, mini-batch size, GAE parameters ($\\lambda$), and coefficients ($c_1, c_2$).\n",
    "*   **Solutions**:\n",
    "    *   **Use Standard Defaults:** Start with widely used values ($\\epsilon=0.2$, $\\lambda=0.95$, Adam optimizer with learning rates around $3e-4$, $K \\approx 4-10$ epochs, reasonable batch/mini-batch sizes).\n",
    "    *   **Tuning:** Experiment systematically, particularly with $\\epsilon$ and learning rates. Larger $\\epsilon$ allows bigger policy changes but risks instability; smaller $\\epsilon$ is more conservative.\n",
    "    *   **Adaptive Epsilon/KL:** Some PPO variants adapt $\\epsilon$ or use a KL penalty term (similar to one TRPO variant) instead of hard clipping.\n",
    "\n",
    "**Challenge: Sample Inefficiency (On-Policy)**\n",
    "*   **Problem:** Although better than REINFORCE/A2C due to multiple epochs, it's still on-policy and less sample efficient than off-policy methods like SAC or DQN on tasks where off-policy learning is viable.\n",
    "  **Solutions**:\n",
    "    *   **Increase Data per Iteration:** Collect more steps per iteration (`STEPS_PER_ITERATION`) before updating.\n",
    "    *   **Increase Epochs:** Perform more updates per batch (K), but monitor for overfitting or policy divergence.\n",
    "    *   **Consider Off-Policy Alternatives:** If interaction is very expensive, explore algorithms like SAC or TD3.\n",
    "\n",
    "**Challenge: Value Function Accuracy**\n",
    "*   **Problem:** The accuracy of the advantage estimates depends heavily on the critic's accuracy. An inaccurate critic can lead to poor policy updates.\n",
    "   **Solutions**:\n",
    "    *   **Tune Critic Learning:** Adjust the critic learning rate, number of critic update epochs per policy update, or use different optimization techniques.\n",
    "    *   **Network Architecture:** Ensure the critic network has sufficient capacity.\n",
    "    *   **Shared vs. Separate Networks:** Experiment with sharing initial layers between actor and critic (can improve efficiency) vs. fully separate networks (can sometimes provide more stability).\n",
    "\n",
    "**Challenge: Implementation Details Matter**\n",
    "*   **Problem:** Small details like advantage standardization, observation/reward normalization, learning rate scheduling, and network initialization can significantly impact performance.\n",
    "*   **Solutions**:\n",
    "    *   **Standardize Advantages:** Usually beneficial.\n",
    "    *   **Normalize Observations/Rewards:** Often crucial, especially for continuous control tasks with varying scales.\n",
    "    *   **Learning Rate Annealing:** Linearly decaying the learning rate over training can improve stability.\n",
    "    *   **Careful Initialization:** Use appropriate weight initialization schemes (e.g., Orthogonal initialization).\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "Proximal Policy Optimization (PPO) stands out as a highly effective and widely adopted reinforcement learning algorithm. By introducing the clipped surrogate objective, it achieves the stability of trust region methods like TRPO while maintaining the simplicity and scalability of first-order optimization. Its actor-critic structure, combined with techniques like Generalized Advantage Estimation and the ability to perform multiple updates per batch of data, leads to robust performance and improved sample efficiency compared to simpler on-policy methods.\n",
    "\n",
    "PPO's versatility across discrete and continuous action spaces, along with its relative ease of implementation and tuning compared to TRPO, has made it a go-to algorithm for numerous applications, from complex control tasks and game playing to the critical task of aligning large language models with human values via RLHF. While still subject to the inherent limitations of on-policy learning regarding sample efficiency, PPO represents a powerful and practical approach within the policy optimization family."
   ]
  }
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